AI 中文总结
研究自动驾驶车辆交互自动测试问题，提出EVITA方法，利用多目标优化生成场景，最小化场景复杂性，有效测试多交互AV，揭示安全关键场景，实验表明该方法能触发更多样交互，提高揭示安全关键行为可能性。
AI 中文摘要
自动驾驶车辆（AV）必须经过全面测试以满足高安全标准，避免危及乘客和道路使用者。基于场景的测试在虚拟仿真环境中实现驾驶场景，是一种经济高效的现场测试替代方案。常见的基于场景的测试方法设定环境和周边交通来测试单个AV。近期研究表明，测试单个AV的方法会遗漏多AV交互产生的关键行为。有效测试n向交互场景的方法必须应对多AV带来的组合爆炸问题。本文提出EVITA方法，利用多目标优化生成触发多样AV交互的场景，同时最小化场景复杂性，有效测试多交互AV并揭示当前方法忽略的安全关键场景。实验结果证实，EVITA比现有方法触发更多样的AV交互，提高了揭示安全关键行为的可能性。
英文摘要
Autonomous vehicles (AVs) must be thoroughly tested to meet high safety standards and avoid endangering both AV passengers and road users. Scenario-based testing implements driving scenarios in virtual simulation environments as a cost-effective alternative to field testing. Common scenario-based testing approaches set the environment and the surrounding traffic and test a single AV. Recent studies show that the approaches that test single AVs miss critical behaviors that emerge from interactions among multiple AVs. Effective approaches to test scenarios that emerge from n-way interactions must address the combinatorial explosion that the presence of multiple AVs further exacerbates. In this paper, we propose EVITA, an approach that leverages multi-objective optimization to generate scenarios that trigger multiple and diverse AVs interactions, while minimizing the complexity of the generated scenarios, to effectively test multiple interacting AVs and reveal safety-critical scenarios that current approaches overlook. The experimental results that we discuss in this paper confirm that EVITA triggers a higher variety of AVs interactions than state-of-the-art approaches, thus improving the likelihood to reveal safety-critical behaviors.
